2024-09-03 16:09:00 +09:00
|
|
|
import numpy as np
|
|
|
|
from collections import deque
|
|
|
|
|
2023-09-27 15:45:31 -07:00
|
|
|
class FirstOrderFilter:
|
|
|
|
# first order filter
|
|
|
|
def __init__(self, x0, rc, dt, initialized=True):
|
|
|
|
self.x = x0
|
|
|
|
self.dt = dt
|
|
|
|
self.update_alpha(rc)
|
|
|
|
self.initialized = initialized
|
|
|
|
|
|
|
|
def update_alpha(self, rc):
|
|
|
|
self.alpha = self.dt / (rc + self.dt)
|
|
|
|
|
|
|
|
def update(self, x):
|
|
|
|
if self.initialized:
|
|
|
|
self.x = (1. - self.alpha) * self.x + self.alpha * x
|
|
|
|
else:
|
|
|
|
self.initialized = True
|
|
|
|
self.x = x
|
|
|
|
return self.x
|
2024-09-03 16:09:00 +09:00
|
|
|
|
|
|
|
class MyMovingAverage:
|
|
|
|
def __init__(self, window_size, value=None):
|
|
|
|
self.window_size = window_size
|
|
|
|
if (value is not None):
|
|
|
|
self.values = deque([value] * window_size, maxlen=window_size)
|
|
|
|
self.sum = value * window_size
|
|
|
|
self.result = value
|
|
|
|
else:
|
|
|
|
self.values = deque(maxlen=window_size)
|
|
|
|
self.sum = 0
|
|
|
|
self.result = 0
|
|
|
|
|
|
|
|
def set(self, value):
|
|
|
|
self.values.clear()
|
|
|
|
self.values.append(value)
|
|
|
|
self.sum = value
|
|
|
|
self.result = value
|
|
|
|
return value
|
|
|
|
|
|
|
|
def set_all(self, value):
|
|
|
|
self.values = deque([value] * self.window_size, maxlen=self.window_size)
|
|
|
|
self.sum = value * self.window_size
|
|
|
|
self.result = value
|
|
|
|
return value
|
|
|
|
|
|
|
|
def process(self, value, median=False):
|
|
|
|
self.values.append(value)
|
|
|
|
self.sum = sum(self.values)
|
|
|
|
self.result = float(np.median(self.values)) if median else float(self.sum) / len(self.values)
|
|
|
|
return self.result
|